Phil Jones and the Great Leap Forward

The other key network in the seminal Jones et al 1990 on urbanization (relied upon in AR4) is their Chinese network. The idea that China between 1954 and 1983 – the age of Chairman Mao and the Great Leap Forward – could have achieved consistency in temperature measurement that eluded the U.S. observing system (with changing times of observation, instruments etc) is a conceit that seems absurd on its face. However Peterson 2003 in a recent literature review held the Jones Chinese network as one of only a few “homogeneous” networks. Jones et al 1990 described their QC procedures as follows:

The stations were selected on the basis of station history; we selected those with few, if any changes in instrumentation, location or observation times.

In this case, I have been able to track down third-party documentation on stations used in Jones’ China network and it is impossible that Jones et al could have carried out the claimed QC procedures. NDP039 states the following:

Few station records included in the PRC data sets can be considered truly homogeneous. Even the best stations were subject to minor relocations or changes in observing times, and many have undoubtedly experienced large increases in urbanization. Fortunately, for 59 of the stations in the 65-station network, station histories (see Table 1) are available to assist in proper interpretation of trends or jumps in the data; however, station histories for the 205-station network are not available. In addition, examination of the data from the 65-station data set has uncovered evidence of several undocumented station moves (Sects. 6 and 10).

I have plotted locations of the 65-station network and, while the stations are in the same general area as the 205-station network, in a first look, few seem to match. Thus, it appears that many, if not most, of the stations in the Jones network are from stations for which there are no station histories and that lack of homogeneity is characteristic of the stations for which there is a station history. Here are details.

We assembled a network of 42 station pairs of rural and urban sites in the eastern half of China (Fig 1c). The data cover the period 1954-83. The 84 stations were selected form a 260-station temperature set recently compiled under the US Department of Energy and PRC Academy od Sciences Joint Project on the Greenhouse Effect. [15 – Koomanoff, et al. 1988, BAMS 69, 1301-1308]. The stations were selected on the basis of station history; we selected those with few, if any changes in instrumentation, location or observation times. All 84 records were complete for the 30-year period. The urban stations were in regions with populations of over 0.5 million, whereas for the rural stations populations mostly less than 0.1 million (according to 1984 population statisitcs). From the 42-station rural network. we formed an average RCHI for eastern China using the iunverse-weighting scheme. The 42-station urban network UCHI was averaged in the same way. Using the gridded data from [1], a regional time series JCHI was developed for the region encompassing 15 grid points. An average series VCHI for the region making use of 32 stations was developed form the data in [4]

The reference Koomanoff et al 1988 is available online here , but really wasn’t much help.

I located the Chinese data partly by chance. While I was browsing information from GHCN v1 (NDP041) in connection with Russia, I noticed the following mention on page 41 to a Chinese data set that looked like it might be connected to Jones.

I googled “60-station China temperature” and starting hitting paydirt thruugh two related references – TR055 and data set NDP039. NDP039 contains two Chinese networks – one with 60 sites (updated in 1997 to 65 sites) and one with 205 sites. Is it possible that Jones used a different 260-series Chinese network obtained at the same time. Impossible. This has to be the source of the Jones data. A 1997 report obtained by googling stated:

The first data product, Two Long-Term Instrumental Climatic Data Bases of the PRC (Tao et al, 1991, 1997) was published initially in 1991 and updated in 1997. This numeric data package (NDP) now includes data through 1993. The Institute of Atmospheric Physics(IAP) of the CAS provided records from 367 stations, partitioned into two networks of 65 and 205 stations….

NDP039
NDP039 is online here and contains some extremely important statements that are relevant for evaluation of the QC. Recall that Jones et al 1990 stated:

The stations were selected on the basis of station history; we selected those with few, if any changes in instrumentation, location or observation times.

NDP039 says of the 205-station network:

Unfortunately, station histories are not currently available for any of the stations in the 205-station network; therefore, details regarding instrumentation, collection methods, changes in station location or observing times, and official data sources are not known.

NDP039 to its credit provides access to individual station histories for 59 stations in the 65-site network. Links from this page go to photocopies of the original station history (Excellent!). Gaps for each station in the 65-station network (and they are not inconsiderable) are listed here.

Few station records included in the PRC data sets can be considered truly homogeneous. Even the best stations were subject to minor relocations or changes in observing times, and many have undoubtedly experienced large increases in urbanization. Fortunately, for 59 of the stations in the 65-station network, station histories (see Table 1) are available to assist in proper interpretation of trends or jumps in the data; however, station histories for the 205-station network are not available. In addition, examination of the data from the 65-station data set has uncovered evidence of several undocumented station moves (Sects. 6 and 10).

Only a portion of the questionable values found in the data have been thoroughly researched and edited via communication with CAS. Most questionable values have been left intact and flagged (Sects. 6 and 9) so that the user may determine how to treat them. Users are strongly advised to check data values for associated flags that may have been assigned. Not doing so may to lead to spurious analytical results.

I plotted up the locations of the 65-site network (for which there are station histories) obtaining the following.
A listing of sites collated from the CDIAC information together with 1 km by 1 km populations (kindly provided by D Linton) is in china2.xls Warwick Hughes will probably be able to identify these locations with WMO site numbers. I’ve looked in detail at many of the locations and have been unable to locate Jones sites in the 65-site network with station histories and thus come from the 205-station network lacking histories. The conclusion that many of the sites lack station histories is unavoidable.
Locations of 65-site network.

31 Comments

Steve,
We are licensees of the LandScan 2005 database (http://www.ornl.gov/sci/landscan/). This gives the world’s population on a 30-arcsecond (roughly 1 km by 1 km) grid. I extracted the population of the grid cells containing your 270 sites.

Steve, with the testimony you have given in Washington, is there anyone you are talking to about the need to have some research funded that is not inside the Hockey Team Lockbox? I am thinking mainly of a new look at surface temps. The only thing that keeps us from being in a total fantasyland is that the satellite temps are not in the hands of the Hockey Team. As the satellite and surface measures continue to diverge, we will be separated into two camps, each relying on different measures of the “truth.” Given the insanity of this whole issue, that is the best we can hope for at present–that there are two sides.

But the non-HT side needs to have a better measure of surface temps. One that uses data that are available for examination and has been researched for surface instument reliability.

These stations are located in eastern China where the world’s highest rate of urban growth has occured over the past century. The rate has accellerated since Mao’s death. People say that if you only go to China once a year, that you will not recognize it. This is not far from the truth.

When I was in Shanghai in September and October last year, it was my first trip since 1998. The city had expanded in 3 dimensions to such an extent that it was mind boggling. In 1998 the population estimate was 12 million people. Today, I heard estimates ranging from 15 to 19 million inhabitants. I believe that the actual population is closer to the high end than to the low end of these estimates. Any census badly underestimates populations by the time it is published.

By selecting stations in eastern China, I am certain that Jones has built into his data a large amount of urban growth. Jones’ claim that the station data is homogeneous flies in the face of the reality of growth in eastern China.

Re: 1
D. F. Linton,
Is it possible to increase the area included around each station? The reason that I am asking is that some stations may appear to be rural at a 30 arc second resolution but may in fact be in a rice field adjacent to a city and thus be within the UHI.

I think what is needed is to take a statistically valid sample of temp stations and do an in-depth and site-visit analysis of them to see if they can be relied on to measure 100 years of temp history. Roy Spenser did something like this in his article about the California Central Valley and the effects of land use on temp measurements.

Selecting Russia and China as areas with homogenous temperature records just repeats a theme of a kind of distain for all things American or at least Western. Jones appears to be unable to resist slapping the West in the face with another veiled bogus charge. It’s the horrid emissions, or the depraved record keeping – take your pick – that makes the West “evil”, in Jones’ eyes anyway. Only the Communists can do this stuff right.

I think I have it figured out.
Jones is defining lack of evidence that a station moved, as evidence that it did not.
Therefore, the lack of a station history, is considered positive evidence that no changes occurred at that station.

In general the sites are within the most heavily populated part of China with very few out in the desolate Western areas. Even in “rural” parts of the East, development patterns and population density would qualify as low income suburban by Western standards. In that part of the country, you are never far away from other people. Even in farming areas, the properties are 10s of an acre or less, sometimes much less, with a home (at least one home) on each plot. Very small scale farming is the norm in Eastern China. And industry is scattered throughout.

You are doing great work on this site. The surface record has bothered me for a number of years, though I haven’t had the wherewithal
to look into it in great depth. This kind of work has been sorely needed within the climate science community. It’s really a shame
that it has to come from without. Keep it up.

A comment and a question. First, it is well past time that surface temperature record receive the scrutiny that the rather more transparent ballon and MSU satellite data have received. Thank you for your efforts in this area despite the difficulties involved in recovering the raw data.

Question. In this series of blogs about the Jones constuctions of surface temperature networks, has the point been that these results of rather dubious homogeneity been used to rejigger the US surface record? Or have I missed the point entirely. The US record has been highly problematic in that it has stubbornly refused to show a strong warming signal in the late 20th century vs that seen in the 1910-1950 time frame. Having it look more like the global records would be highly useful to some. This is beginning to look like malfeasance or willful delusion.

#18. There’s a big difference between malfeasance and bias. Authors proposing that past temperatures be reduced justify this in terms of time-of-observation bias and they can support these arguments. The difficulty arises because the amount of the adjustment seems to be about the same size as the effect being measured. I get the impression that the authors are much more zealous in finding reasons to adjust past temperatures than the opposite and are too quick to accept very slight and weakly argued papers arguing against (say) UHI. I don’t think that it’s helpful to worry about malfeasance. This doesn’t appear to be a case where authors are withholding adverse verification statistics.

That means that adjusting the data for whatever reason is the same as the effect being measured to me means that adjustment for level makes a geochemical disappear explicitly means the geochemical anomalies are spurious. Real anomalies don’t disappear if some minor ajustments to levels is done.

Thaaat means that the temperature anomalies (-.4K to +.4K) are simply random noise which will vary if “adjustments” are made.

But the historical climate changes are real but the cause may not be temperature but the latitude of the anomaly varying over time.

Means and standard deviations of each station’s respective monthly values were calculated. Values lying 3.5 or more standard deviations away from their respective means were flagged if not corroborated by temperatures at neighboring stations

I understand perfectly why the temp, precip, cloud cover, and other quantifiable data would under go standard deviation
checks, but weather conditions unlike some other types of data can have fluccuations due to geography. If they applied this procedures in Central Europe they could have big problems. Cloud cover and or fog due can keep one station in a “cold regime” relative to its adjacent stations for days on end. I’ve seen in Germany where a small drainage wind can clear stratiform clouds from one valley, while the next valley 3km away remains in 0/0 conditions. Even in the Great Plains, where the terrain in fairly even, snow cover can affect surface temperatures and cloud cover. I wonder if researchers appply thier QA checks with a intimate understanding of the local geography and all of its attendent quirks. By smoothing the instrument readings to this degree, the researchers are creating a climate picture that in the long run doesn’t exist.

On the other hand, the data validation subroutines built into the short range forecast models for NOAA and the Air Force concentrate on obvious errors (wind gusts to 500mph, hour to hour temp increases over over 15 deg F, etc..) the observations are kicked back to the observer for correction or validation; if the station is automated and had preceived errors, the readings in question were given a reality check.

That reminds me of a story about the airport at Halifax. When they were looking to relocate the airport (to its current location) they tried to find a location with the clearest weather. Apparently fog was a problem at the old airport. After searching the area they selected a location 35 km from Halifax and began construction. After they cut down the trees the fog rolled in.

I found this on Michael Crichton’s website from his 2005 testimony to the US Senate.

For a person with a medical background, accustomed to this degree of rigor in research, the protocols of climate science appear considerably more relaxed. In climate science, it’s permissible for raw data to be “touched,” or modified, by many hands. Gaps in temperature and proxy records are filled in. Suspect values are deleted because a scientist deems them erroneous. A researcher may elect to use parts of existing records, ignoring other parts. But the fact that the data has been modified in so many ways inevitably raises the question of whether the results of a given study are wholly or partially caused by the modifications themselves.

Means and standard deviations of each station’s respective monthly values were calculated. Values lying 3.5 or more standard deviations away from their respective means were flagged if not corroborated by temperatures at neighboring stations.

Given the cavalier handling of statistics by Jones and Mann in the Svalbard affair, this makes me nervous.

First, and most important, standard estimators of standard deviation can gravely underestimate the standard deviation of a time series with either short or long term autocorrelation. This is particularly a problem with series with high Hurst coefficients, such as many climate measurement series. It is not unheard of for temperature series to have a true standard deviation which is three times as large as the estimate in the usual manner. Which, of course, means that if the SD is three times as large, the 3.5 standard deviation cutoff used by Jones is actually only a bit more than one standard deviation of variance when measured correctly.

Second, in addition to assuming lack of autocorrelation, normal statistics assume a trendless dataset, which is certainly not true for climate data. This also increases the standard deviation, and must be accounted for separately.

Third, many datasets contain bpth gaps and station changes. Estimates of means and standard deviations must be done carefully, and will certainly be less accurate.

Fourth, I don’t know about this paper, but in the past Jones has used the standard deviation of a 30-year reference period (say 1951-1980) as his measuring stick. Depending on the characteristics of the 30 year period chosen, the results can be remarkably different, both in mean and standard deviation. This has a subtle effect when you are looking for evidence of UHI, which is that the stations removed will tend to be at the opposite end of the dataset from the period chosen. And in a period of generally rising temperatures, this method will selectively remove cold temperatures from the early part of the record. This is not a good strategy to use when minor differences in the trends are important.

Finally, consider the numbers. Odds of greater than 3.5 standard deviation are .00046. Eighty-four stations, average record length say 60 years, 12 months in a year, that’s 28 months of perfectly good data that would be thrown out even if Jones’ numbers were right … but they’re not right, it’s much worse than that. IF the standard deviation is actually 1.5 times the incorrect estimate there will be over 1,000 months thrown out. And if the standard deviation is twice as large as estimated, which is not uncommon in temperature records, the number of (mostly cold) months wrongly excluded goes up to almost 5,000.

#27. Warwick, that’s great. I notice that many of the identified sites do not have a companion within both 1 degree lat and 1 degree long. I wonder if we’ll ever find out what series Jones used in this article. I haven’t received any acknowledgement to my request from Jones; I’ll file a complaint with Nature fairly soon if I don’t hear anything,

RE: #27 – A number of them outside of China, especially quite a cluster in Bangladesh. Two on Taiwan, one right at Keelung (major port which started its boom after WW2) and another just south of the high tech mecca of Hsin Chu (a post 1970s development). Quite a cluster in the part of Japan which is visible, we all know what has happened to the “rural” areas of Soutwestern Japan since the 1950s (massive growth in industry and the residential developments to accomodate all its people).

The studies finding a large urbanisation effect [Kalnay & Cai, 2003, Zhou et al., 2004] are based on comparison of observations with reanalyses, and assume that any difference is entirely due to biases in the observations. A comparison of HadCRUT data with the ERA- 40 reanalysis [Simmons et al., 2004] demonstrated that there were sizable biases in the reanalysis, so this assumption cannot be made, and the most reliable way to investigate possible urbanisation biases is to compare rural and urban station series.

Zhou et al:

It is impossible to find a corresponding rural station for most of the urban ones, especially in eastern and southern China. Consequently, if using the rural’€”urban difference to estimate the UHI, one possibly is comparing temperature between two different urban stations at regional scales or between two different regions at large scales.

Jones 90:

We assembled a network of 42 station pairs of rural and urban sites in the eastern half of China.

6 Trackbacks

[…] in April 2007 (See cache here). The release of this data confirmed my previous surmise (see CA here, Feb 22, 2007) that the following representation in Jones et al 1990 about the Chinese network was […]

[…] actions obtained this information. We discussed Jones et al 1990 in a number of posts. We observed here that Jones et al 1990 made untrue claims on the quality control for their Chinese network (the […]